import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


A						=			logging.get_logger(__name__)





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		lowerCAmelCase_     =   ['input_features']
		def __init__(       self     :     Optional[Any]     ,  snake_case__     :     Dict=8_0     ,  snake_case__     :     Dict=1_6_0_0_0     ,  snake_case__     :     Union[str, Any]=1_6_0     ,  snake_case__     :     Union[str, Any]=3_0     ,  snake_case__     :     List[str]=4_0_0     ,  snake_case__     :     Optional[Any]=0.0     ,  snake_case__     :     List[Any]=False     ,  **snake_case__     :     List[str]     ,  )      ->	Dict:
				super().__init__(
				    feature_size=snake_case__     ,  sampling_rate=snake_case__     ,  padding_value=snake_case__     ,  return_attention_mask=snake_case__     ,  **snake_case__     ,  )
				_lowerCamelCase				   =  n_fft
				_lowerCamelCase				   =  hop_length
				_lowerCamelCase				   =  chunk_length
				_lowerCamelCase				   =  chunk_length * sampling_rate
				_lowerCamelCase				   =  self.n_samples // hop_length
				_lowerCamelCase				   =  sampling_rate
				_lowerCamelCase				   =  mel_filter_bank(
				    num_frequency_bins=1 + n_fft // 2     ,  num_mel_filters=snake_case__     ,  min_frequency=0.0     ,  max_frequency=8000.0     ,  sampling_rate=snake_case__     ,  norm='slaney'     ,  mel_scale='slaney'     ,  )
		def   _snake_case       (       self     :     Dict     ,  snake_case__     :     np.array		)      ->	np.ndarray:
				_lowerCamelCase				   =  spectrogram(
				    snake_case__     ,  window_function(self.n_fft     ,  'hann'		)     ,  frame_length=self.n_fft     ,  hop_length=self.hop_length     ,  power=2.0     ,  mel_filters=self.mel_filters     ,  log_mel='log10'     ,  )
				_lowerCamelCase				   =  log_spec[:, :-1]
				_lowerCamelCase				   =  np.maximum(snake_case__     ,  log_spec.max() - 8.0		)
				_lowerCamelCase				   =  (log_spec + 4.0) / 4.0
				return log_spec
		@staticmethod
		# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
		def   _snake_case       (       snake_case__     :     List[np.ndarray]     ,  snake_case__     :     List[np.ndarray]     ,  snake_case__     :     float = 0.0		)      ->	List[np.ndarray]:
				if attention_mask is not None:
						_lowerCamelCase				   =  np.array(snake_case__     ,  np.intaa		)
						_lowerCamelCase				   =  []

						for vector, length in zip(snake_case__     ,  attention_mask.sum(-1		)		):
								_lowerCamelCase				   =  (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7		)
								if length < normed_slice.shape[0]:
										_lowerCamelCase				   =  padding_value

								normed_input_values.append(snake_case__		)
				else:
						_lowerCamelCase				   =  [(x - x.mean()) / np.sqrt(x.var() + 1e-7		) for x in input_values]

				return normed_input_values
		def __call__(       self     :     Union[str, Any]     ,  snake_case__     :     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,  snake_case__     :     bool = True     ,  snake_case__     :     Optional[int] = None     ,  snake_case__     :     Optional[Union[str, TensorType]] = None     ,  snake_case__     :     Optional[bool] = None     ,  snake_case__     :     Optional[str] = "max_length"     ,  snake_case__     :     Optional[int] = None     ,  snake_case__     :     Optional[int] = None     ,  snake_case__     :     Optional[bool] = None     ,  **snake_case__     :     List[Any]     ,  )      ->	BatchFeature:

				if sampling_rate is not None:
						if sampling_rate != self.sampling_rate:
								raise ValueError(
								    f"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
								    f""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
								    f""" was sampled with {self.sampling_rate} and not {sampling_rate}."""		)
				else:
						logger.warning(
						    'It is strongly recommended to pass the `sampling_rate` argument to this function. '
						    'Failing to do so can result in silent errors that might be hard to debug.'		)

				_lowerCamelCase				   =  isinstance(snake_case__     ,  np.ndarray		) and len(raw_speech.shape		) > 1
				if is_batched_numpy and len(raw_speech.shape		) > 2:
						raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""		)
				_lowerCamelCase				   =  is_batched_numpy or (
				    isinstance(snake_case__     ,  (list, tuple)		) and (isinstance(raw_speech[0]     ,  (np.ndarray, tuple, list)		))
				)

				if is_batched:
						_lowerCamelCase				   =  [np.asarray([speech]     ,  dtype=np.floataa		).T for speech in raw_speech]
				elif not is_batched and not isinstance(snake_case__     ,  np.ndarray		):
						_lowerCamelCase				   =  np.asarray(snake_case__     ,  dtype=np.floataa		)
				elif isinstance(snake_case__     ,  np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
						_lowerCamelCase				   =  raw_speech.astype(np.floataa		)

				# always return batch
				if not is_batched:
						_lowerCamelCase				   =  [np.asarray([raw_speech]		).T]

				_lowerCamelCase				   =  BatchFeature({'input_features': raw_speech}		)

				# convert into correct format for padding

				_lowerCamelCase				   =  self.pad(
				    snake_case__     ,  padding=snake_case__     ,  max_length=max_length if max_length else self.n_samples     ,  truncation=snake_case__     ,  pad_to_multiple_of=snake_case__     ,  return_attention_mask=return_attention_mask or do_normalize     ,  )

				# zero-mean and unit-variance normalization
				if do_normalize:
						_lowerCamelCase				   =  self.zero_mean_unit_var_norm(
						    padded_inputs['input_features']     ,  attention_mask=padded_inputs['attention_mask']     ,  padding_value=self.padding_value     ,  )
						_lowerCamelCase				   =  np.stack(padded_inputs['input_features']     ,  axis=0		)

				# make sure list is in array format
				_lowerCamelCase				   =  padded_inputs.get('input_features'		).transpose(2     ,  0     ,  1		)

				_lowerCamelCase				   =  [self._np_extract_fbank_features(snake_case__		) for waveform in input_features[0]]

				if isinstance(input_features[0]     ,  snake_case__		):
						_lowerCamelCase				   =  [np.asarray(snake_case__     ,  dtype=np.floataa		) for feature in input_features]
				else:
						_lowerCamelCase				   =  input_features

				if return_attention_mask:
						# rescale from sample (48000) to feature (3000)
						_lowerCamelCase				   =  padded_inputs['attention_mask'][:, :: self.hop_length]

				if return_tensors is not None:
						_lowerCamelCase				   =  padded_inputs.convert_to_tensors(snake_case__		)

				return padded_inputs



		def   _snake_case       (       self     :     Dict		)      ->	Dict[str, Any]:
				_lowerCamelCase				   =  copy.deepcopy(self.__dict__		)
				_lowerCamelCase				   =  self.__class__.__name__
				if "mel_filters" in output:
						del output["mel_filters"]
				return output
def 	lowerCamelCase					(	UpperCamelCase     :				int = 60_08_51_47_51_43							)   ->    int:

		try:
				_lowerCamelCase				   =  int(UpperCamelCase							)
		except (TypeError, ValueError):
				raise TypeError('Parameter n must be int or castable to int.'							)
		if n <= 0:
				raise ValueError('Parameter n must be greater than or equal to one.'							)
		_lowerCamelCase				   =  1
		_lowerCamelCase				   =  2
		while i * i <= n:
				while n % i == 0:
						_lowerCamelCase				   =  i
						n //= i
				i += 1
		if n > 1:
				_lowerCamelCase				   =  n
		return int(UpperCamelCase							)


if __name__ == "__main__":
				print(F'''{solution() = }''')